检索结果-内蒙古大学图书馆

Conference on computer vision and Pattern Recognition (CVPR)

作者： Bin Fu Junjun He Jianjun Wang Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://***/fubinfb/NTF.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparison of Hand Gesture Recognition Models Combining Supervised Learning and Reinforcement Learning

Comparison of Hand Gesture Recognition Models Combining Supe...

引用

Latin America Congress on Computational intelligence (LA-CCI)

作者： Cristian Gabriel Bastidas Kevin Alexander Pérez Ángel Leonardo Valdivieso Caraguay Lorena Isabel Barona López Marco E. Benalcázar Departamento de Informática y Ciencias de la Computación (DICC) Artificial Intelligence and Computer Vision Research Lab Escuela Politécnica Nacional Quito Ecuador

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Hand Gesture Recognition (HGR) using Electromyography (EMG) signals is a crucial component of Human-computer interaction, enabling natural communication between humans and technology in various applications, such as prosthetics, robotics, and rehabilitation devices. While supervised learning techniques like Convolutional Neural Networks (CNNs) have been extensively explored in HGR systems and have achieved high levels of accuracy, reinforcement learning (RL) stands out for its ability to learn from interaction and solve sequential decision problems. Algorithms such as Q-Learning and Deep Q-Network (DQN) have shown promising results in HGR using EMG. This research aims to compare the performance of two reinforcement learning techniques: DQN and DDQN in HGR using the EMG-EPN-612 dataset, evaluating the accuracy of the models in both recognition and classification. Key contributions include the design of a reward function for this context and the use of a pre-existing convolutional neural network for fine-tuning. The results show that models with fine-tuning, alongside DQN and DDQN, exhibited similar values in the proposed metrics. A 3.61% improvement in recognition accuracy was observed for the DQN model and a 3.45% improvement for the DDQN model compared to the baseline model. However, there are no significant improvements for the DQN model compared to the baseline model. In conclusion, it was found that DQN outperformed DDQN in this scenario.

关键词： Hands Training Accuracy Q-learning Supervised learning Gesture recognition Electromyography Real-time systems Convolutional neural networks Robots

来源：评论

学校读者我要写书评

暂无评论

Fairness Through Controlled (Un)Awareness in Node Embeddings

arXiv

引用

arXiv 2024年

作者： Vetter, Dennis Forth, Jasper Roig, Gemma Dell, Holger Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt am Main Germany Theoretical Computer Science Group Goethe University Frankfurt Frankfurt am Main Germany

Graph representation learning is central for the application of machine learning (ML) models to complex graphs, such as social networks. Ensuring ‘fair’ representations is essential, due to the societal implications and the use of sensitive personal data. In this paper, we demonstrate how the parametrization of the CrossWalk algorithm influences the ability to infer a sensitive attributes from node embeddings. By fine-tuning hyperparameters, we show that it is possible to either significantly enhance or obscure the detectability of these attributes. This functionality offers a valuable tool for improving the fairness of ML systems utilizing graph embeddings, making them adaptable to different fairness paradigms. © 2024, CC BY-NC-SA.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

Polyp-PVT:Polyp Segmentation with Pyramid vision Transformers

引用

CAAI artificial intelligence Research 2023年第1期2卷 1-15页

作者： Bo Dong Wenhai Wang Deng-Ping Fan Jinpeng Li Huazhu Fu Ling Shao College of Computer Science Nankai UniversityTianjin 300350China Shanghai Artificial Intelligence Laboratory Shanghai 200232China Computer Vision Lab Inception Institute of Artificial IntelligenceAbu DhabiUnited Arab Emirates Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore UCAS-Terminus AI Lab Terminus GroupChongqing 400042China

Most polyp segmentation methods use convolutional neural networks(CNNs)as their backbone,leading to two key issues when exchanging information between the encoder and decoder:(1)taking into account the differences in contribution between different-level features,and(2)designing an effective mechanism for fusing these *** existing CNN-based methods,we adopt a transformer encoder,which learns more powerful and robust *** addition,considering the image acquisition influence and elusive properties of polyps,we introduce three standard modules,including a cascaded fusion module(CFM),a camouflage identification module(CIM),and a similarity aggregation module(SAM).Among these,the CFM is used to collect the semantic and location information of polyps from high-level features;the CIM is applied to capture polyp information disguised in low-level features,and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area,thereby effectively fusing cross-level *** proposed model,named Polyp-PVT,effectively suppresses noises in the features and significantly improves their expressive *** experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations(e.g.,appearance changes,small objects,and rotation)than existing representative *** proposed model is available at https://***/DengPingFan/Polyp-PVT.

关键词： polyp segmentation pyramid vision transformer colonoscopy computer vision

来源：评论

学校读者我要写书评

暂无评论

Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Bin Fu Fanghua Yu Anran Liu Zixuan Wang Jie Wen Junjun He Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences The University of Hong Kong Harbin Institute of Technology Shenzhen Shanghai Artificial Intelligence Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content dis-entanglement paradigm and employ the Generative Adver-sarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to modelfont generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework. named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfbIMSD-Font.

关键词： Costs Noise Diffusion processes Transforms Manuals Diffusion models Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Pipe-BD: Pipelined Parallel Blockwise Distillation

Pipe-BD: Pipelined Parallel Blockwise Distillation

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Hongsun Jang Jaewon Jung Jaeyong Song Joonsang Yu Youngsok Kim Jinho Lee Department of Electrical and Computer Engineering Seoul National University Department of Artificial Intelligence Yonsei University Department of Computer Science Yonsei University CLOVA Image Vision CLOVA AI Lab NAVER

Training large deep neural network models is highly challenging due to their tremendous computational and mem-ory requirements. Blockwise distillation provides one promising method towards faster convergence by splitting a large model into multiple smaller models. In state-of-the-art blockwise distillation methods, training is performed block-by-block in a data-parallel manner using multiple GPUs. To produce inputs for the student blocks, the teacher model is executed from the beginning until the current block under training. However, this results in a high overhead of redundant teacher execution, low GPU utilization, and extra data loading. To address these problems, we propose Pipe-BD, a novel parallelization method for blockwise distillation. Pipe-BD aggressively utilizes pipeline parallelism for blockwise distillation, eliminating redundant teacher block execution and increasing per-device batch size for better resource utilization. We also extend to hybrid parallelism for efficient workload balancing. As a result, Pipe-BD achieves significant acceleration without modifying the mathematical formulation of blockwise distillation. We implement Pipe-BD on PyTorch, and experiments reveal that Pipe-BD is effective on multiple scenarios, models, and datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

arXiv

引用

arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Effects on Hand Gesture Recognition Accuracy after Severe Cranial Trauma

Effects on Hand Gesture Recognition Accuracy after Severe Cr...

引用

IEEE Ecuador Technical Chapters Meeting (ETCM)

作者： Jonathan A. Zea Luis Gabriel Macias Santillán Lorena Isabel Barona López Ángel Leonardo Valdivieso Caraguay Marco E. Benalcázar Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Artificial Intelligence and Computer Vision Research Lab Quito Ecuador

This study examines the impact of severe cranial trauma on the accuracy of Hand Gesture Recognition (HGR) models based on electromyography (EMG) signals. The study focuses on a single subject, who suffered left-sided hemiplegia, and exhibits limited mobility and spasticity in the left hand. EMG samples corresponding to six hand gestures from his both forearms were collected using the Myo Armband. The 2023HGR5-CNN-LSTM HGR model was used to evaluate the recognition accuracy of these samples. Results showed significantly lower HGR performance in the left side (10.67% recognition accuracy) compared to the right side (56% recognition accuracy). As the selected model was trained with EMG data only from right arms, a channel mapping to adapt the EMG signals from the left side was also developed. Unfortunately, the channel mapping implemented did not improve the recognition accuracy (9.33%). These findings highlight the challenges in achieving high performance HGR for individuals with cranial injuries. It might also point out that developing myoelectric prostheses using datasets from unaffected individuals may be inadequate for training robust models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation

HTML: Hybrid Temporal-scale Multimodal Learning Framework fo...

引用

International Conference on computer vision (ICCV)

作者： Mingfei Han Yali Wang Zhihui Li Lina Yao Xiaojun Chang Yu Qiao ReLER AAII UTS Data61 CSIRO Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shanghai AI Laboratory Shanghai China Shandong Artificial Intelligence Qilu University of Technology Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence

Referring Video Object Segmentation (RVOS) is to segment the object instance from a given video, according to the textual description of this object. However, in the open world, the object descriptions are often diversified in contents and flexible in lengths. This leads to the key difficulty in RVOS, i.e., various descriptions of different objects are corresponding to different temporal scales in the video, which is ignored by most existing approaches with single stride of frame sampling. To tackle this problem, we propose a concise Hybrid Temporal-scale Multimodal Learning (HTML) framework, which can effectively align lingual and visual features to discover core object semantics in the video, by learning multimodal interaction hierarchically from different temporal scales. More specifically, we introduce a novel inter-scale multimodal perception module, where the language queries dynamically interact with visual features across temporal scales. It can effectively reduce complex object confusion by passing video context among different scales. Finally, we conduct extensive experiments on the widely used benchmarks, including Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences, where our HTML achieves state-of-the-art performance on all these datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning

Skeleton-in-Context: Unified Skeleton Sequence Modeling with...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Xinshun Wang Zhongbin Fang Xia Li Xiangtai Li Chen Chen Mengyuan Liu Sun Yat-sen University National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Department of Computer Science ETH Zurich S-Lab Nanyang Technological University Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning. However, skeleton sequence modeling via in-context learning remains unexplored. Directly applying existing in-context models from other areas onto skeleton sequences fails due to the similarity between inter-frame and cross-task poses, which makes it exceptionally hard to perceive the task correctly from a subtle context. To address this challenge, we propose Skeleton-in-Context (SiC), an effective framework for in-context skeleton sequence modeling. Our SiC is able to handle multiple skeleton-based tasks simultaneously after a single training process and accomplish each task from context according to the given prompt. It can further generalize to new, unseen tasks according to customized prompts. To facilitate context perception, we additionally propose a task-unified prompt, which adaptively learns tasks of different natures, such as partial joint-level generation, sequence-level prediction, or 2D-to-3D motion prediction. We conduct extensive experiments to evaluate the effectiveness of our SiC on multiple tasks, including motion prediction, pose estimation, joint completion, and future pose estimation. We also evaluate its generalization capability on unseen tasks such as motion-in-between. These experiments show that our model achieves state-of-the-art multi-task performance and even outperforms single-task methods on certain tasks.

关键词： Training Solid modeling Three-dimensional displays Silicon carbide Computational modeling Pose estimation Predictive models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：